Challenge overview

The Grounded CoMPaT Recognition (GCR) is a compositional 3D vision task that aims to collectively recognize and ground compositions of materials on parts of 3D objects. This task is based on the 3DCoMPaT dataset, a large-scale dataset composed of stylized 3D objects and associated 2D renderings.
We propose two variations of this task: GCR-Coarse and GCR-Fine, which are based on coarse-grained and fine-grained 3D segmentations of the 3DCoMPaT models.
We highly encourage participants of the challenge to enter and submit to both tracks of the challenge.

Dataset

The 3DCoMPaT dataset for both challenge tracks will be made available soon.

Submission

Submission links on CodaLab for both tracks will be made available soon, which will include detailed instructions and evaluation criteria.

Rules

TBA.

Awards

TBA.

Q&A

If you encounter any technical issue related to the challenge, or if you're missing critical information, please open a ticket on our GitHub repository.

Topics

Besides the CoMPaT challenge, the C3DV workshop also accepts papers in relation with compositional 3D vision. The workshop will include a poster and an oral session for related works. Topics of this workshop include but are not limited to:

• Deep learning methods for compositional 3D vision
• Self-supervised learning for compositional 3D vision
• Visual relationship detection in 3D scenes
• Zero-shot recognition/detection of compositional 3D visual concepts
• Novel problems in 3D vision and compositionality
• Text/composition to 3D generation
• Text/composition-based editing of 3D scenes/objects
• Language-guided 3D visual understanding (objects, relationships, ...)
• Transfer learning for compositional 3D Vision
• Multimodal pre-training for 3D understanding
• ...

The submitted 4-page abstracts will be peer-reviewed in CVPR format. Abstracts will be presented in the workshop poster session, and a portion of the accepted papers will be orally presented.

Submission

Paper submissions will be handled with CMT through the following link:

Microsoft CMT: C3DV-2023

Please select the appropriate track (archival or non-archival) and check for the relevant timelines in the dates section.

Paper awards

TBA.